Most of the known optical character recognition (OCR) methods have essentially two characteristics in common. First, each includes a recognition methodology that starts with a pre-segmentation stage in which a line of text is first segmented into words and/or characters, followed by a separate character recognition stage. The use of word and/or character segmentation with subsequent feature extraction for image recognition is described, for example, in U.S. Pat. No. 5,438,630 issued Aug. 1, 1995 to Chen et al. Second, each method is language dependent requiring that both separate training and recognition systems be designed specifically for each language.
A template-based OCR method has been suggested which does not require pre-segmentation. However, this method has difficulty recognizing patterns because of intra-class variability of the patterns (see PCT/US93/01843 (international publication number WO93/18483), page 8) and is not language independent.
There are several references in the literature to OCR systems based on statistical modeling using Hidden Markov Models (HMMs). See, for example, 1! E. Levin and R. Pieraccini, "Dynamic Planar Warping for Optical Character Recognition," IEEE Int. Conf. Acoustics, Speech, Signal Processing, San Francisco, Calif., pp. 111-149--111-152, March 1992; 2! J. C. Anigbogu and A. Belaid, "Performance Evaluation of an HMM Based OCR System," Proc. 11th Int. Pattern Recognition, The Hague, The Netherlands, pp. 565-568, August 1992; 3! G. Kopec and P. Chou, "Document Image Decoding Using Markov Source Models," IEEE Int. Conf. Acoustics, Speech, Signal Processing, Minneapolis, Minn., pp. V-85-88, April 1993; and 4! O. E. Agazzi and S. Kuo, "Hidden Markov Model Based Optical Character Recognition in the Presence of Deterministic Transformations," Pattern Recognition, Vol. 26, No. 12, pp. 1813-1826, 1993. However, none of these references suggests that these systems are independent of the script or language used or that the features extracted for analysis are independent of language type. Further, the use of HMMs for character recognition presents problems as HMM models are more suited for use with continuous speech recognition (CSR) systems where there is only one independent variable, i.e., time, while in the above-identified referenced articles 1!-4!, the system responds to two independent variables.
On-line handwriting recognition systems have been designed which compute feature vectors as functions of time. See, for example, 5! T. Starner, J. Makhoul, R. Schwartz and G. Chou; "On-Line Cursive Handwriting Recognition Using Speech Recognition Methods; IEEE International Conference on Acoustics, Speech, and Signal Processing, Adelaide, Austrailia, Apr. 19-22, 1994, Vol. V. pp. 125-128. However, OCR applications are faced with the problem of recognizing a whole page of text which presents a two-dimensional problem for which there is no obvious way of defining a feature vector as a function of one independent variable. So the problem of how to apply HMMs to OCR needs to be satisfactorily addressed.